<italic>IntelliDaM</italic>: A Machine Learning-Based Framework for Enhancing the Performance of Decision-Making Processes. A Case Study for Educational Data Mining

Abstract

Nowadays, both predictive and descriptive modelling play a key role in decision-making processes in almost every branch of activity. In this article we are introducing $IntelliDaM$ , a generic machine learning-based framework useful for improving the performance of data mining tasks and subsequently enhancing decision-making processes. Through its components designed for feature analysis, unsupervised and supervised learning-based data mining, $IntelliDaM$ facilitates hidden knowledge discovery from data. Intensive research has been conducted in the field of educational data mining, as education institutions are interested in constantly adapting their educational programs to the needs of society by improving the quality of managerial decisions, course instructors’ decision-making, or information gathering for course design. The present work conducts a longitudinal educational data mining study by applying $IntelliDaM$ to real data collected at Babeş-Bolyai University, Romania, for a Computer Science course. The problem of mining educational data has been thoroughly examined using the proposed framework, with the goal of analysing students’ performance. A very good performance has been achieved for the classification task (an $F1$ score of around 92%), and the results also highlighted a statistically significant performance improvement by using a technique for selecting discriminative data features. The performed study confirmed that $IntelliDaM$ could be a useful instrument in educational environments, particularly for improving decision-making processes, like designing courses, the setup of efficient examinations, avoiding plagiarism, or offering support regarding stress management.

Keywords

• Data mining
• educational data mining
• machine learning
• students’ performance analysis
• prediction